title: “DA5020.P1.Tapiawala” team: Samir Tapiawala, Omar Waid output: html_document
library(dplyr)##
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library(lubridate)##
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## date, intersect, setdiff, unionlibrary(XML)
library(RCurl)##
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library(countrycode)Question 1)
Download the COVID-19 case distribution (in XML formal): https://opendata.ecdc.europa.eu/ covid19/casedistribution/xml/. Load the XML file into a browser or text editing tool and inspect it. Explore the data set as you see fit and get a sense of the data. Note that you are working with a live dataset that is updated daily. Therefore it is best to ensure that your results are data driven
Answer 1)
SAMPLE from xml file:
-> Components:
- Date: day/month/year
- Case: no. of new cases/per day
- Deaths: no. of deaths /per day
- Countries and Territories: country full name
- geoID: 2 digit ISO Code
- Country Territory Code: 3 digit ISO Code
- Population Data: 2018 census data
- Continent: cotinent name
-> How many DBs are used in this? 1) COVID 19: new cases and deaths 2) Population 3) World country DB: ?ISO to divide world into continent followed by country territory, country name and geoID?
Question 2)
Load the data into R (directly from the URL) and create two linked tibbles: one for country and the other for covid_19_data that contains each country’s reported case. The country tibble should contain the following: countriesAndTerritories, countryterritoryCode (primary key), popData2018, continentExp. The covid_19_data tibble should contain: id (auto incremented value that will serve as the primary key), countryterritoryCode(foreign key), dateRep, cases, deaths. You can link/join both tibbles by using the countryterritoryCode
url_covid <- "https://opendata.ecdc.europa.eu/covid19/casedistribution/xml/" # converting url into a vector
temp_covid <- getURL(url_covid) # creating a temporary file for the url
covid_parse <- xmlParse(temp_covid) # parsing the data from the temporary file
#view(covid_parse)
covid_xml <- xmlRoot(covid_parse) # to access top-level XMLNode in the file
xmlName(covid_xml) # to get the name of the top-level XMLNode## [1] "records"covid_entire <- xmlToDataFrame(covid_xml)
view(covid_entire)
glimpse(covid_entire)## Rows: 23,428
## Columns: 11
## $ dateRep <fct> 14/06/2020, 13/06/2020, 12/06/2020, 11/06/202…
## $ day <fct> 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1…
## $ month <fct> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, …
## $ year <fct> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 202…
## $ cases <fct> 556, 656, 747, 684, 542, 575, 791, 582, 915, …
## $ deaths <fct> 5, 20, 21, 21, 15, 12, 30, 18, 9, 6, 24, 5, 8…
## $ countriesAndTerritories <fct> Afghanistan, Afghanistan, Afghanistan, Afghan…
## $ geoId <fct> AF, AF, AF, AF, AF, AF, AF, AF, AF, AF, AF, A…
## $ countryterritoryCode <fct> AFG, AFG, AFG, AFG, AFG, AFG, AFG, AFG, AFG, …
## $ popData2018 <fct> 37172386, 37172386, 37172386, 37172386, 37172…
## $ continentExp <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asi…# All variables are represented as 'Factor" type. In future, we might have to format these variables into approproate types to run the codes and analysiscovid_entire %>% select(7:10) %>% sapply(function(x){length(unique(x))})## countriesAndTerritories geoId countryterritoryCode
## 210 210 207
## popData2018
## 206# We observed four countries/territories codes are missing. We need to add those codes in the database
# Anguilla -> "AIA"
# Bonaire, Saint Eustatius and Saba -> "BES"
# Falkland_Islands_(Malvinas) -> "FLK"
# Western_Sahara -> "ESH"
# In order to do so, we will have to change variable type for both Countryname and countrycode from 'Factor' to 'Character' ... using 'Factor', it's giving error
covid_entire$countriesAndTerritories <- as.character(covid_entire$countriesAndTerritories)
covid_entire$countryterritoryCode <- as.character(covid_entire$countryterritoryCode)
for (i in 1:nrow(covid_entire)){ # This will help to iterate through entire dataframe columns
if(covid_entire[i,"countryterritoryCode"]==''){ # if country code value is blank/missing
covid_entire[i,"countryterritoryCode"] <-
countrycode(covid_entire[i,"countriesAndTerritories"], origin ='country.name' , destination = "iso3c")
# replace/fill the missing values from 'countrycode' package
#print(covid_view[i,"countriesAndTerritories"])
}
}
view(covid_entire)# Rechecking, whether countrycodes numbers are matching or no:
covid_entire %>% select(7:10) %>% sapply(function(x){length(unique(x))})## countriesAndTerritories geoId countryterritoryCode
## 210 210 210
## popData2018
## 206# Population data of same countries missing:
# Anguilla -> 14731
# Bonaire, Saint Eustatius and Saba -> 19549
# Falkland_Islands_(Malvinas) -> 3234
# Western_Sahara -> 567402
# Eritrea -> 6050000
# Before doing so, we have to convert variable type of 'popData2018'. Mentioned as 'Factor'... to convert into 'numeric'
covid_entire$popData2018 <- as.numeric(as.character(covid_entire$popData2018))
for (p in 1:nrow(covid_entire)){
if(covid_entire[p,"countriesAndTerritories"] == "Anguilla"){
covid_entire[p,"popData2018"] <- 14731
}
if(covid_entire[p,"countriesAndTerritories"] == "Bonaire, Saint Eustatius and Saba"){
covid_entire[p,"popData2018"] <- 19549
}
if(covid_entire[p,"countriesAndTerritories"] == "Falkland_Islands_(Malvinas)"){
covid_entire[p,"popData2018"] <- 3234
}
if(covid_entire[p,"countriesAndTerritories"] == "Western_Sahara"){
covid_entire[p,"popData2018"] <- 567402
}
if(covid_entire[p,"countriesAndTerritories"] == "Eritrea"){
covid_entire[p,"popData2018"] <- 6050000
}
}
view(covid_entire) # Rechecking, whether Population data numbers are matching or no:
covid_entire %>% select(7:10) %>% sapply(function(x){length(unique(x))})## countriesAndTerritories geoId countryterritoryCode
## 210 210 210
## popData2018
## 210# In the dataset, Country Name, geoID and Country Code (3 digits) are reduntant. Removing geoID column/variable from the dataset.
# Keeping Country Name: required to identify countries based on codes.
# Also, keeping Country Code - required as 'Primary Key' in future analysis.
covid_entire = subset(covid_entire, select = -c(geoId))
glimpse(covid_entire)## Rows: 23,428
## Columns: 10
## $ dateRep <fct> 14/06/2020, 13/06/2020, 12/06/2020, 11/06/202…
## $ day <fct> 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1…
## $ month <fct> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, …
## $ year <fct> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 202…
## $ cases <fct> 556, 656, 747, 684, 542, 575, 791, 582, 915, …
## $ deaths <fct> 5, 20, 21, 21, 15, 12, 30, 18, 9, 6, 24, 5, 8…
## $ countriesAndTerritories <chr> "Afghanistan", "Afghanistan", "Afghanistan", …
## $ countryterritoryCode <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AF…
## $ popData2018 <dbl> 37172386, 37172386, 37172386, 37172386, 37172…
## $ continentExp <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asi…# We found some negative values under 'cases' and 'deaths' columns. Seems it is typo error. since we can not verify the data, it is advisable to delete those observations to avoid any misinterpretation of the data.
# Before doing so, we have to convert variable types of 'cases' and 'deaths'. Mentioned as 'Factor'... to convert into 'numeric'
covid_entire$cases <- as.integer(as.character(covid_entire$cases))
covid_entire$deaths <- as.integer(as.character(covid_entire$deaths))
# (1) covid_entire1 <- covid_entire[covid_entire$cases >= 0,] # creating subset to include values >= 0, thus removing all negative values with typo error
# (2) covid_entire <- covid_entire1[covid_entire1$deaths >= 0,]
#covid_entire
#view(covid_entire)# By running above codes: (1) and (2) removed around 15 negative values, but at the same time, the process removed entire rows from the dataset. Realised that, by removing negative values of cases, positive values of deaths were getting removed from the dataset. So, decided to replace negative values by ZERO rather than losing important information from other variables.
for (n in 1:nrow(covid_entire)){ # This will help to iterate through entire dataframe columns
if(covid_entire[n,"cases"]< 0){
covid_entire[n,"cases"] <- 0
}
if(covid_entire[n,"deaths"]< 0){
covid_entire[n,"deaths"] <- 0
}
}
view(covid_entire) ========================== x ======================================
Answer 2)
…To create two tibbles:
- Country: country
- name (countries and territories),
- country territory (3 digit code)- Primary Key,
- Population data 2018,
- continentExp
# 'country' tibble:
country <- tibble(
"countryName" = covid_entire$countriesAndTerritories,
"countryCode" = covid_entire$countryterritoryCode,
"population" = covid_entire$popData2018,
"region" = covid_entire$continentExp
) %>% distinct()
#country <- select(countryName, countryCode, population, region)
view(country)
glimpse(country)## Rows: 210
## Columns: 4
## $ countryName <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Angola",…
## $ countryCode <chr> "AFG", "ALB", "DZA", "AND", "AGO", "AIA", "ATG", "ARG", "…
## $ population <dbl> 37172386, 2866376, 42228429, 77006, 30809762, 14731, 9628…
## $ region <fct> Asia, Europe, Africa, Europe, Africa, America, America, A…- COVID 19: covid_19_data
- id (autoincremented value) - Primary Key
- country territory code - Foreign key
- dateRep
- cases
- deaths
# creating tibble using select() function
covid_19_data <- tibble(covid_entire %>%
select(
"reportDate" = dateRep,
"countryCode" = countryterritoryCode,
"newCases" = cases,
"deaths" = deaths) %>%
mutate(dataKey = row_number()) # this code will generate rowID - autoincremental value
)
view(covid_19_data)
glimpse(covid_19_data)## Rows: 23,428
## Columns: 5
## $ reportDate <fct> 14/06/2020, 13/06/2020, 12/06/2020, 11/06/2020, 10/06/202…
## $ countryCode <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "…
## $ newCases <dbl> 556, 656, 747, 684, 542, 575, 791, 582, 915, 787, 758, 75…
## $ deaths <dbl> 5, 20, 21, 21, 15, 12, 30, 18, 9, 6, 24, 5, 8, 8, 3, 11, …
## $ dataKey <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…# To link/join both tibbles by using the countryterritoryCode... using countryCode, which is Prmary key in country tibble while foreign key in covid_19_data tibble
covid_update <- tibble(
right_join(covid_19_data, country, by = "countryCode")
)
view(covid_update)
glimpse(covid_update)## Rows: 23,428
## Columns: 8
## $ reportDate <fct> 14/06/2020, 13/06/2020, 12/06/2020, 11/06/2020, 10/06/202…
## $ countryCode <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "…
## $ newCases <dbl> 556, 656, 747, 684, 542, 575, 791, 582, 915, 787, 758, 75…
## $ deaths <dbl> 5, 20, 21, 21, 15, 12, 30, 18, 9, 6, 24, 5, 8, 8, 3, 11, …
## $ dataKey <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
## $ countryName <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan…
## $ population <dbl> 37172386, 37172386, 37172386, 37172386, 37172386, 3717238…
## $ region <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asi…========================== x ======================================
Question 3)
Create a function called worldwideCases() that displays: a) the total cases worldwide, b) the number of new cases within the past day (grouped by continent)
Answer 3)
# total new cases: addition of all observations in the variable newCases in last 24 hours(per day)
# using sum() argument
# Dates of Reporting (dateRep) are as 'Factor' in the dataset, we need to convert them into 'date' category
# group_by(continent): newcases in last 24 hours
# asked for two types of output: 'total cases worldwide' and 'continent wise'
worldwideCases <- function(dt) {
date_parameter <- covid_entire[format(as.Date(covid_entire$dateRep, '%d/%m/%Y'), '%m/%d/%Y') == dt,]
# creating this variable to match the date and use for output related to 'continents cases'
date_parameter2 <- covid_entire[format(as.Date(covid_entire$dateRep, '%d/%m/%Y'), '%m/%d/%Y') <= dt,]
# creating this variable to match the date and use for output related to 'world cases'
casesWorld <- sum(date_parameter2$cases) # using sum() function to calculate total cases worldwide
casesContinent <- as.data.frame(date_parameter %>% group_by(continentExp) %>% summarise(cases = sum(cases))) # creating data frame to represent all continenets total in tabular format
world_wide_cases = paste("Total cases worldwide till", dt, ":", casesWorld)
# formating output message for 'world wide cases till the date searched for'
continent_wide_cases = paste("Number of cases in last 24 hours:", casesContinent$continentExp, ":", casesContinent$cases)
# formating output message for 'continent wise cases in last 24 hours of the date searched for'
#print(date_parameter)
#print(casesWorld)
print(world_wide_cases)
print(continent_wide_cases)
print(casesContinent)
}
worldwideCases("06/14/2020")## [1] "Total cases worldwide till 06/14/2020 : 7766640"
## [1] "Number of cases in last 24 hours: Africa : 8037"
## [2] "Number of cases in last 24 hours: America : 69850"
## [3] "Number of cases in last 24 hours: Asia : 38064"
## [4] "Number of cases in last 24 hours: Europe : 17448"
## [5] "Number of cases in last 24 hours: Oceania : 12"
## continentExp cases
## 1 Africa 8037
## 2 America 69850
## 3 Asia 38064
## 4 Europe 17448
## 5 Oceania 12# formatted in a common way by which people enter the date: month-day-year========================== x ======================================
Question 4)
Create visualizations that show the progression of the cases and the mortality rate in each continent.
Answer 4)
Visualisation:in each continent : to add population of each countries in that continent
1. progression of case: to add no. of cases of each countries in that continent
2. mortality rate: deaths/population: to add no. of deaths of each countries in that continent divide by population# Mortality Rate is displayed in the scientific form rather than numeric. This is ? due to Population data is converted into intger type??? Checked and rechecked many times - that's the reason!! Quite surprising, weird and annoying! Converting it back to 'factor' and then, will use as.integer() in the codes.
#covid_entire$popData2018 <- as.factor(as.character(covid_entire$popData2018))
class(covid_entire$popData2018)## [1] "numeric"#covid_entire$popData2018 <- as.numeric(as.character(covid_entire$popData2018))options(scipen = 999)
# Running this to stop formating values into 'scientific numbers'
continentMortality <- tibble(
covid_entire %>% group_by(dateRep, continentExp) %>%
summarise(cases = sum(cases), deaths = sum(deaths),
population = sum(popData2018)) %>%
mutate("mortalityRate" = ((deaths/population) * 100000)) %>% # 'denominator' for Mortality rate specific to any disease
select(dateRep, continentExp, cases, deaths, population, mortalityRate))
continentMortality## # A tibble: 899 x 6
## dateRep continentExp cases deaths population mortalityRate
## <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 01/01/2020 Africa 0 0 336526764 0
## 2 01/01/2020 America 0 0 727597933 0
## 3 01/01/2020 Asia 0 0 3998054219 0
## 4 01/01/2020 Europe 0 0 623974756 0
## 5 01/01/2020 Oceania 0 0 29877869 0
## 6 01/01/2020 Other 0 0 3000 0
## 7 01/02/2020 Africa 0 0 336526764 0
## 8 01/02/2020 America 2 0 727597933 0
## 9 01/02/2020 Asia 2110 46 3998054219 0.00115
## 10 01/02/2020 Europe 6 0 623974756 0
## # … with 889 more rows#view(continentMortality)
# Ref: Mortality Rate: https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section3.html#continentMortality %>% ggplot() + geom_point(mapping = aes(y = mortalityRate, x = cases, color = continentExp)) +
#geom_smooth(mapping = aes(y = mortalityRate, x = cases, color = continentExp)) + facet_wrap(~ continentExp)
continentMortality %>%
ggplot(mapping = aes(y = mortalityRate, x = cases, color = continentExp)) +
geom_point() + geom_smooth(aes(color = mortalityRate)) + facet_wrap(~ continentExp) +
ylim(0, 0.8) +
labs(x = "No. of Cases", y = "Mortality Rate / 100,000 Population",
title = "Progression of Cases & Mortality Rate: Continent-wise") ## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 5 rows containing non-finite values (stat_smooth).## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at -0.67## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 0.4489## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at -0.67## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.67## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 10 rows containing missing values (geom_smooth).
#subtitle = paste(":")** ANALYSIS of Mortality Rate (per 100,000 population) and Progression of Cases: Continent-wise:
Africa: Eventhough being the second largest continent, Africa has not crossed more than 10,000 cases per day. Mortality rate is controlled well under 0.05. Whether the numbers are less because of undocumented cases or les screening compared to other continents? Or it is so because of the warm climate? Other theory can also be because Africa has Malaria as endemic disease and people have consumed medicine Chloroquine that has been restricting or rather decreasing ‘virulence’ of the disease? Deeper studies required…
America: This continent seems to be worst hit amongst all continents with number of cases goign as high as 80,000 per day. Currently USA, BRAZIL, PERU are amongst top 10 worst hit countries. The graph also suggests that, as number of cases increases, the mortality rate also increases. As the number of cases crosses 30,000 mark, the mortality rate remains between o.3 to 0.4. The highest mortality recorded for this continent is 0.55
Asia: Considering that Asia is the largest continent and highest population compared to America and Europe continents, still the number of cases have not crossed 40,000 per day. Eventhough the disease started in Asia, relatively low number of cases than America and lower mortality rate (0.02-0.03 - similar to Africa) indicates well-controlled measures and management in containing the disease.
Europe: Europe was the worst hit continent when the pandemic started spreading. The graph shows steep rise of mortality compared to America’s graph. America and Europe - both continents have almost similar mortality rate, but the major diofference is number of cases per day. That means with less number of cases, deaths were more - attributed to more senior population residing in Europe. That’s why the ‘virulence’ of the diseases seems more in Europe. This is more evident when we comapre Europe and Asia, where both continents have similar number of cases yet major difference is mortality rate (0.7 being the highest in Europe v/s 0.02-0.03 in Asia)
Oceania: Australia and New Zealand contributing to major population of Oceania, but the countries are less densely populated makes common measure of social distancing possible. This seems to be the major contributor to control the disease and avoid spread. The continent seems to be less affected by the disease. New Zealand recently declared that it is free from the disease.
Others: Japanese Cruise-ship: This graph is of the Japanese Cruise-ship that had COVID-19 positive patient and the disease spread in the ship. Total passengers plus crew were around 3000. The data suggests that the ship had very less mortality rate inspite of all patients and passengers were strangled inside the ship and affected around 700 passengers.
========================== x ======================================
Question 5)
Display the ten countries with the highest number of cases. Analyze the data and indicate the date of the first reported case and the ratio of cases-to-fatalities for each country (use supporting visualizations)
Answer 5)
- sum(cases)
- group_by(countries)
- descend()
- first date of the first reported case ? how to display?
- the ratio of cases-to-fatalities for each country: deaths/cases: cumulative?.. has to be cumulative as asked for highest number of cases
- visualisation: bar chart for cases and line for ratio? or both lines?
topTen <- tibble(
covid_entire %>% group_by(countriesAndTerritories) %>%
# group_by countries since we want top 10 countries
summarise(cases = sum(cases), deaths = sum(deaths)) %>%
# both cases and deaths have to be totalled to calculate total number of cases/deaths till date
mutate("case_to_fatalities" = ((deaths / cases)*100)) %>%
# adding new column in the tibble by calculating the Ratio of case-to-fatalities: generally considered as per 100: https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section3.html
select(countriesAndTerritories, cases, deaths, case_to_fatalities))
# selecting required columns
topTen <- head(topTen[order(topTen$cases, decreasing=T), ], 10)
# since we want to find ten countries having highest number of cases - using head() function and arranging total cases in decreasing order
topTen## # A tibble: 10 x 4
## countriesAndTerritories cases deaths case_to_fatalities
## <chr> <dbl> <dbl> <dbl>
## 1 United_States_of_America 2074526 115436 5.56
## 2 Brazil 850514 42720 5.02
## 3 Russia 520129 6829 1.31
## 4 India 320922 9195 2.87
## 5 United_Kingdom 294900 41662 14.1
## 6 Spain 244690 29054 11.9
## 7 Italy 236651 34301 14.5
## 8 Peru 225132 6498 2.89
## 9 Germany 186269 8787 4.72
## 10 Iran 184955 8730 4.72topTenCountries <- covid_entire[covid_entire$countriesAndTerritories %in% topTen$countriesAndTerritories,]
topTenCountries$"case_to_fatalities" <- topTenCountries$deaths/topTenCountries$cases
#view(topTenCountries)
topTenCountries## dateRep day month year cases deaths countriesAndTerritories
## 3051 14/06/2020 14 6 2020 21704 892 Brazil
## 3052 13/06/2020 13 6 2020 25982 909 Brazil
## 3053 12/06/2020 12 6 2020 30412 1239 Brazil
## 3054 11/06/2020 11 6 2020 32913 1274 Brazil
## 3055 10/06/2020 10 6 2020 32091 1272 Brazil
## 3056 09/06/2020 9 6 2020 15654 679 Brazil
## 3057 08/06/2020 8 6 2020 18921 525 Brazil
## 3058 07/06/2020 7 6 2020 27075 904 Brazil
## 3059 06/06/2020 6 6 2020 30830 1005 Brazil
## 3060 05/06/2020 5 6 2020 30916 1473 Brazil
## 3061 04/06/2020 4 6 2020 28633 1349 Brazil
## 3062 03/06/2020 3 6 2020 28936 1262 Brazil
## 3063 02/06/2020 2 6 2020 11598 623 Brazil
## 3064 01/06/2020 1 6 2020 16409 480 Brazil
## 3065 31/05/2020 31 5 2020 33274 956 Brazil
## 3066 30/05/2020 30 5 2020 26928 1124 Brazil
## 3067 29/05/2020 29 5 2020 26417 1156 Brazil
## 3068 28/05/2020 28 5 2020 20599 1086 Brazil
## 3069 27/05/2020 27 5 2020 16324 1039 Brazil
## 3070 26/05/2020 26 5 2020 11687 807 Brazil
## 3071 25/05/2020 25 5 2020 15813 653 Brazil
## 3072 24/05/2020 24 5 2020 16508 965 Brazil
## 3073 23/05/2020 23 5 2020 20803 1001 Brazil
## 3074 22/05/2020 22 5 2020 18508 1188 Brazil
## 3075 21/05/2020 21 5 2020 19951 888 Brazil
## 3076 20/05/2020 20 5 2020 17408 1179 Brazil
## 3077 19/05/2020 19 5 2020 13140 674 Brazil
## 3078 18/05/2020 18 5 2020 7938 485 Brazil
## 3079 17/05/2020 17 5 2020 14919 816 Brazil
## 3080 16/05/2020 16 5 2020 15305 824 Brazil
## 3081 15/05/2020 15 5 2020 13944 844 Brazil
## 3082 14/05/2020 14 5 2020 11385 749 Brazil
## 3083 13/05/2020 13 5 2020 9258 881 Brazil
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## 22488 09/05/2020 9 5 2020 26957 1510 United_States_of_America
## 22489 08/05/2020 8 5 2020 28369 2239 United_States_of_America
## 22490 07/05/2020 7 5 2020 24128 2353 United_States_of_America
## 22491 06/05/2020 6 5 2020 23841 2144 United_States_of_America
## 22492 05/05/2020 5 5 2020 22593 1252 United_States_of_America
## 22493 04/05/2020 4 5 2020 24972 1297 United_States_of_America
## 22494 03/05/2020 3 5 2020 29288 1317 United_States_of_America
## 22495 02/05/2020 2 5 2020 33955 2062 United_States_of_America
## 22496 01/05/2020 1 5 2020 29917 2040 United_States_of_America
## 22497 30/04/2020 30 4 2020 27326 2611 United_States_of_America
## 22498 29/04/2020 29 4 2020 24132 2110 United_States_of_America
## 22499 28/04/2020 28 4 2020 22541 1369 United_States_of_America
## 22500 27/04/2020 27 4 2020 26857 1687 United_States_of_America
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## 22502 25/04/2020 25 4 2020 21352 1054 United_States_of_America
## 22503 24/04/2020 24 4 2020 26543 3179 United_States_of_America
## 22504 23/04/2020 23 4 2020 17588 1721 United_States_of_America
## 22505 22/04/2020 22 4 2020 37289 2524 United_States_of_America
## 22506 21/04/2020 21 4 2020 28065 1857 United_States_of_America
## 22507 20/04/2020 20 4 2020 24601 1772 United_States_of_America
## 22508 19/04/2020 19 4 2020 32922 1856 United_States_of_America
## 22509 18/04/2020 18 4 2020 30833 3770 United_States_of_America
## 22510 17/04/2020 17 4 2020 31667 2299 United_States_of_America
## 22511 16/04/2020 16 4 2020 30148 4928 United_States_of_America
## 22512 15/04/2020 15 4 2020 26922 2408 United_States_of_America
## 22513 14/04/2020 14 4 2020 25023 1541 United_States_of_America
## 22514 13/04/2020 13 4 2020 27620 1500 United_States_of_America
## 22515 12/04/2020 12 4 2020 28391 1831 United_States_of_America
## 22516 11/04/2020 11 4 2020 35527 2087 United_States_of_America
## 22517 10/04/2020 10 4 2020 33901 1873 United_States_of_America
## 22518 09/04/2020 9 4 2020 33323 1922 United_States_of_America
## 22519 08/04/2020 8 4 2020 30613 1906 United_States_of_America
## 22520 07/04/2020 7 4 2020 30561 1342 United_States_of_America
## 22521 06/04/2020 6 4 2020 25398 1146 United_States_of_America
## 22522 05/04/2020 5 4 2020 34272 1344 United_States_of_America
## 22523 04/04/2020 4 4 2020 32425 1104 United_States_of_America
## 22524 03/04/2020 3 4 2020 28819 915 United_States_of_America
## 22525 02/04/2020 2 4 2020 27103 1059 United_States_of_America
## 22526 01/04/2020 1 4 2020 24998 909 United_States_of_America
## 22527 31/03/2020 31 3 2020 21595 661 United_States_of_America
## 22528 30/03/2020 30 3 2020 18360 318 United_States_of_America
## 22529 29/03/2020 29 3 2020 19979 484 United_States_of_America
## 22530 28/03/2020 28 3 2020 18695 411 United_States_of_America
## 22531 27/03/2020 27 3 2020 16797 246 United_States_of_America
## 22532 26/03/2020 26 3 2020 13963 249 United_States_of_America
## 22533 25/03/2020 25 3 2020 8789 211 United_States_of_America
## 22534 24/03/2020 24 3 2020 11236 119 United_States_of_America
## 22535 23/03/2020 23 3 2020 8459 131 United_States_of_America
## 22536 22/03/2020 22 3 2020 7123 80 United_States_of_America
## 22537 21/03/2020 21 3 2020 5374 110 United_States_of_America
## 22538 20/03/2020 20 3 2020 4835 0 United_States_of_America
## 22539 19/03/2020 19 3 2020 2988 42 United_States_of_America
## 22540 18/03/2020 18 3 2020 1766 23 United_States_of_America
## 22541 17/03/2020 17 3 2020 887 16 United_States_of_America
## 22542 16/03/2020 16 3 2020 823 12 United_States_of_America
## 22543 15/03/2020 15 3 2020 777 10 United_States_of_America
## 22544 14/03/2020 14 3 2020 511 7 United_States_of_America
## 22545 13/03/2020 13 3 2020 351 10 United_States_of_America
## 22546 12/03/2020 12 3 2020 287 2 United_States_of_America
## 22547 11/03/2020 11 3 2020 271 2 United_States_of_America
## 22548 10/03/2020 10 3 2020 200 5 United_States_of_America
## 22549 09/03/2020 9 3 2020 121 4 United_States_of_America
## 22550 08/03/2020 8 3 2020 95 3 United_States_of_America
## 22551 07/03/2020 7 3 2020 105 2 United_States_of_America
## 22552 06/03/2020 6 3 2020 74 1 United_States_of_America
## 22553 05/03/2020 5 3 2020 34 2 United_States_of_America
## 22554 04/03/2020 4 3 2020 22 3 United_States_of_America
## 22555 03/03/2020 3 3 2020 14 4 United_States_of_America
## 22556 02/03/2020 2 3 2020 20 1 United_States_of_America
## 22557 01/03/2020 1 3 2020 3 1 United_States_of_America
## 22558 29/02/2020 29 2 2020 6 0 United_States_of_America
## 22559 28/02/2020 28 2 2020 1 0 United_States_of_America
## 22560 27/02/2020 27 2 2020 6 0 United_States_of_America
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## 22565 22/02/2020 22 2 2020 19 0 United_States_of_America
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## 22567 20/02/2020 20 2 2020 0 0 United_States_of_America
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## 22575 12/02/2020 12 2 2020 0 0 United_States_of_America
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## 22577 10/02/2020 10 2 2020 0 0 United_States_of_America
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## 22580 07/02/2020 7 2 2020 0 0 United_States_of_America
## 22581 06/02/2020 6 2 2020 1 0 United_States_of_America
## 22582 05/02/2020 5 2 2020 0 0 United_States_of_America
## 22583 04/02/2020 4 2 2020 0 0 United_States_of_America
## 22584 03/02/2020 3 2 2020 3 0 United_States_of_America
## 22585 02/02/2020 2 2 2020 1 0 United_States_of_America
## 22586 01/02/2020 1 2 2020 1 0 United_States_of_America
## 22587 31/01/2020 31 1 2020 1 0 United_States_of_America
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## 22592 26/01/2020 26 1 2020 0 0 United_States_of_America
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## 22594 24/01/2020 24 1 2020 0 0 United_States_of_America
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## 22616 02/01/2020 2 1 2020 0 0 United_States_of_America
## 22617 01/01/2020 1 1 2020 0 0 United_States_of_America
## 22618 31/12/2019 31 12 2019 0 0 United_States_of_America
## countryterritoryCode popData2018 continentExp case_to_fatalities
## 3051 BRA 209469333 America 0.0410984150
## 3052 BRA 209469333 America 0.0349857594
## 3053 BRA 209469333 America 0.0407404972
## 3054 BRA 209469333 America 0.0387081093
## 3055 BRA 209469333 America 0.0396372815
## 3056 BRA 209469333 America 0.0433754951
## 3057 BRA 209469333 America 0.0277469478
## 3058 BRA 209469333 America 0.0333887350
## 3059 BRA 209469333 America 0.0325981187
## 3060 BRA 209469333 America 0.0476452322
## 3061 BRA 209469333 America 0.0471134705
## 3062 BRA 209469333 America 0.0436134918
## 3063 BRA 209469333 America 0.0537161580
## 3064 BRA 209469333 America 0.0292522396
## 3065 BRA 209469333 America 0.0287311414
## 3066 BRA 209469333 America 0.0417409388
## 3067 BRA 209469333 America 0.0437597002
## 3068 BRA 209469333 America 0.0527210059
## 3069 BRA 209469333 America 0.0636486155
## 3070 BRA 209469333 America 0.0690510824
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## 3072 BRA 209469333 America 0.0584565059
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## 3075 BRA 209469333 America 0.0445090472
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## 3086 BRA 209469333 America 0.0687965319
## 3087 BRA 209469333 America 0.0734689885
## 3088 BRA 209469333 America 0.0616909385
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## 3090 BRA 209469333 America 0.0865176640
## 3091 BRA 209469333 America 0.0446253581
## 3092 BRA 209469333 America 0.0599389712
## 3093 BRA 209469333 America 0.0847082495
## 3094 BRA 209469333 America 0.0689321952
## 3095 BRA 209469333 America 0.0602660017
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## 3100 BRA 209469333 America 0.0627493653
## 3101 BRA 209469333 America 0.1019126463
## 3102 BRA 209469333 America 0.1089692102
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## 3105 BRA 209469333 America 0.0586403736
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## 3107 BRA 209469333 America 0.0706205005
## 3108 BRA 209469333 America 0.0666257292
## 3109 BRA 209469333 America 0.0893111639
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## 3113 BRA 209469333 America 0.0686546463
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## 3116 BRA 209469333 America 0.0730569948
## 3117 BRA 209469333 America 0.0601809955
## 3118 BRA 209469333 America 0.0686333534
## 3119 BRA 209469333 America 0.0723542117
## 3120 BRA 209469333 America 0.0633802817
## 3121 BRA 209469333 America 0.0597381342
## 3122 BRA 209469333 America 0.0523560209
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## 3124 BRA 209469333 America 0.0357462020
## 3125 BRA 209469333 America 0.0369068541
## 3126 BRA 209469333 America 0.0712074303
## 3127 BRA 209469333 America 0.0625000000
## 3128 BRA 209469333 America 0.0451745380
## 3129 BRA 209469333 America 0.0298804781
## 3130 BRA 209469333 America 0.0414937759
## 3131 BRA 209469333 America 0.0474137931
## 3132 BRA 209469333 America 0.0387096774
## 3133 BRA 209469333 America 0.0260869565
## 3134 BRA 209469333 America 0.0167464115
## 3135 BRA 209469333 America 0.0312500000
## 3136 BRA 209469333 America 0.0176678445
## 3137 BRA 209469333 America 0.0103626943
## 3138 BRA 209469333 America 0.0218978102
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## 3147 BRA 209469333 America NaN
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## 3150 BRA 209469333 America 0.0000000000
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## 22295 GBR 66488991 Europe 0.0000000000
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## 22300 GBR 66488991 Europe 0.0000000000
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## 22303 GBR 66488991 Europe NaN
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## 22317 GBR 66488991 Europe 0.0000000000
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## 22319 GBR 66488991 Europe 0.0000000000
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## 22322 GBR 66488991 Europe NaN
## 22323 GBR 66488991 Europe 0.0000000000
## 22324 GBR 66488991 Europe NaN
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## 22329 GBR 66488991 Europe NaN
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## 22336 GBR 66488991 Europe NaN
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## 22339 GBR 66488991 Europe NaN
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## 22349 GBR 66488991 Europe NaN
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## 22352 GBR 66488991 Europe NaN
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## 22354 GBR 66488991 Europe NaN
## 22355 GBR 66488991 Europe NaN
## 22356 GBR 66488991 Europe NaN
## 22357 GBR 66488991 Europe NaN
## 22358 GBR 66488991 Europe NaN
## 22359 GBR 66488991 Europe NaN
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## 22361 GBR 66488991 Europe NaN
## 22452 USA 327167434 America 0.0300313234
## 22453 USA 327167434 America 0.0331136160
## 22454 USA 327167434 America 0.0391557051
## 22455 USA 327167434 America 0.0445328418
## 22456 USA 327167434 America 0.0535226359
## 22457 USA 327167434 America 0.0261927532
## 22458 USA 327167434 America 0.0319253879
## 22459 USA 327167434 America 0.0296539621
## 22460 USA 327167434 America 0.0370164429
## 22461 USA 327167434 America 0.0490066225
## 22462 USA 327167434 America 0.0504594142
## 22463 USA 327167434 America 0.0503309969
## 22464 USA 327167434 America 0.0362325714
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## 22467 USA 327167434 America 0.0481114576
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## 22469 USA 327167434 America 0.0815127397
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## 22472 USA 327167434 America 0.0307759627
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## 22474 USA 327167434 America 0.0540439806
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## 22477 USA 327167434 America 0.0785177767
## 22478 USA 327167434 America 0.0362162905
## 22479 USA 327167434 America 0.0428124834
## 22480 USA 327167434 America 0.0484338629
## 22481 USA 327167434 America 0.0651560295
## 22482 USA 327167434 America 0.0653207088
## 22483 USA 327167434 America 0.0840150130
## 22484 USA 327167434 America 0.0772405660
## 22485 USA 327167434 America 0.0638074736
## 22486 USA 327167434 America 0.0362325995
## 22487 USA 327167434 America 0.0630173356
## 22488 USA 327167434 America 0.0560151352
## 22489 USA 327167434 America 0.0789241778
## 22490 USA 327167434 America 0.0975215517
## 22491 USA 327167434 America 0.0899291137
## 22492 USA 327167434 America 0.0554153941
## 22493 USA 327167434 America 0.0519381708
## 22494 USA 327167434 America 0.0449672221
## 22495 USA 327167434 America 0.0607274334
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## 22497 USA 327167434 America 0.0955500256
## 22498 USA 327167434 America 0.0874357699
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## 22500 USA 327167434 America 0.0628141639
## 22501 USA 327167434 America 0.0447567434
## 22502 USA 327167434 America 0.0493630573
## 22503 USA 327167434 America 0.1197679237
## 22504 USA 327167434 America 0.0978508074
## 22505 USA 327167434 America 0.0676875218
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## 22507 USA 327167434 America 0.0720295923
## 22508 USA 327167434 America 0.0563756758
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## 22514 USA 327167434 America 0.0543084721
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## 22521 USA 327167434 America 0.0451216631
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## 22524 USA 327167434 America 0.0317498872
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## 22526 USA 327167434 America 0.0363629090
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## 22533 USA 327167434 America 0.0240072818
## 22534 USA 327167434 America 0.0105909576
## 22535 USA 327167434 America 0.0154864641
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## 22537 USA 327167434 America 0.0204689245
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## 22541 USA 327167434 America 0.0180383315
## 22542 USA 327167434 America 0.0145808019
## 22543 USA 327167434 America 0.0128700129
## 22544 USA 327167434 America 0.0136986301
## 22545 USA 327167434 America 0.0284900285
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## 22548 USA 327167434 America 0.0250000000
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## 22552 USA 327167434 America 0.0135135135
## 22553 USA 327167434 America 0.0588235294
## 22554 USA 327167434 America 0.1363636364
## 22555 USA 327167434 America 0.2857142857
## 22556 USA 327167434 America 0.0500000000
## 22557 USA 327167434 America 0.3333333333
## 22558 USA 327167434 America 0.0000000000
## 22559 USA 327167434 America 0.0000000000
## 22560 USA 327167434 America 0.0000000000
## 22561 USA 327167434 America NaN
## 22562 USA 327167434 America 0.0000000000
## 22563 USA 327167434 America NaN
## 22564 USA 327167434 America NaN
## 22565 USA 327167434 America 0.0000000000
## 22566 USA 327167434 America 0.0000000000
## 22567 USA 327167434 America NaN
## 22568 USA 327167434 America NaN
## 22569 USA 327167434 America NaN
## 22570 USA 327167434 America NaN
## 22571 USA 327167434 America NaN
## 22572 USA 327167434 America NaN
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## 22574 USA 327167434 America 0.0000000000
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## 22577 USA 327167434 America NaN
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## 22579 USA 327167434 America NaN
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## 22581 USA 327167434 America 0.0000000000
## 22582 USA 327167434 America NaN
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## 22588 USA 327167434 America NaN
## 22589 USA 327167434 America NaN
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## 22591 USA 327167434 America 0.0000000000
## 22592 USA 327167434 America NaN
## 22593 USA 327167434 America 0.0000000000
## 22594 USA 327167434 America NaN
## 22595 USA 327167434 America NaN
## 22596 USA 327167434 America NaN
## 22597 USA 327167434 America 0.0000000000
## 22598 USA 327167434 America NaN
## 22599 USA 327167434 America NaN
## 22600 USA 327167434 America NaN
## 22601 USA 327167434 America NaN
## 22602 USA 327167434 America NaN
## 22603 USA 327167434 America NaN
## 22604 USA 327167434 America NaN
## 22605 USA 327167434 America NaN
## 22606 USA 327167434 America NaN
## 22607 USA 327167434 America NaN
## 22608 USA 327167434 America NaN
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## 22610 USA 327167434 America NaN
## 22611 USA 327167434 America NaN
## 22612 USA 327167434 America NaN
## 22613 USA 327167434 America NaN
## 22614 USA 327167434 America NaN
## 22615 USA 327167434 America NaN
## 22616 USA 327167434 America NaN
## 22617 USA 327167434 America NaN
## 22618 USA 327167434 America NaNtopTenCountries <- topTenCountries[order(format(as.Date(topTenCountries$dateRep, '%d/%m/%Y'), '%m/%d/%Y')),]
view(topTenCountries)# to find out 'first case registered' for each country
newtenSub <- topTenCountries %>%
group_by(countryterritoryCode, dateRep, cases) %>%
filter(cases == 1 || cases==2 || cases==3)
unique(newtenSub)## # A tibble: 56 x 11
## # Groups: countryterritoryCode, dateRep, cases [56]
## dateRep day month year cases deaths countriesAndTer… countryterritor…
## <fct> <fct> <fct> <fct> <dbl> <dbl> <chr> <chr>
## 1 21/01/… 21 1 2020 1 0 United_States_o… USA
## 2 25/01/… 25 1 2020 1 0 United_States_o… USA
## 3 27/01/… 27 1 2020 3 0 United_States_o… USA
## 4 28/01/… 28 1 2020 1 0 Germany DEU
## 5 29/01/… 29 1 2020 3 0 Germany DEU
## 6 30/01/… 30 1 2020 1 0 India IND
## 7 31/01/… 31 1 2020 1 0 Germany DEU
## 8 31/01/… 31 1 2020 3 0 Italy ITA
## 9 31/01/… 31 1 2020 2 0 United_Kingdom GBR
## 10 31/01/… 31 1 2020 1 0 United_States_o… USA
## # … with 46 more rows, and 3 more variables: popData2018 <dbl>,
## # continentExp <fct>, case_to_fatalities <dbl>newtenSub[!duplicated(newtenSub$countryterritoryCode),]## # A tibble: 10 x 11
## # Groups: countryterritoryCode, dateRep, cases [10]
## dateRep day month year cases deaths countriesAndTer… countryterritor…
## <fct> <fct> <fct> <fct> <dbl> <dbl> <chr> <chr>
## 1 21/01/… 21 1 2020 1 0 United_States_o… USA
## 2 28/01/… 28 1 2020 1 0 Germany DEU
## 3 30/01/… 30 1 2020 1 0 India IND
## 4 31/01/… 31 1 2020 3 0 Italy ITA
## 5 31/01/… 31 1 2020 2 0 United_Kingdom GBR
## 6 01/02/… 1 2 2020 2 0 Russia RUS
## 7 01/02/… 1 2 2020 1 0 Spain ESP
## 8 20/02/… 20 2 2020 2 2 Iran IRN
## 9 26/02/… 26 2 2020 1 0 Brazil BRA
## 10 07/03/… 7 3 2020 1 0 Peru PER
## # … with 3 more variables: popData2018 <dbl>, continentExp <fct>,
## # case_to_fatalities <dbl># Above is the list of all ten top countries and their first date of case reporting/registeration.cumulativeCovid5 <- tibble(
covid_entire %>%
group_by(countriesAndTerritories, "dateRep" = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d/%Y')) %>%
summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
mutate("cumulative_cases" = cumsum(cases),
"cumulative_deaths" = cumsum(deaths),
"case_to_fatalities" = (cumulative_deaths / cumulative_cases)*100) %>%
select(dateRep, countriesAndTerritories, cases, cumulative_cases,
deaths, cumulative_deaths, case_to_fatalities)
)
cumulativeCovid5## # A tibble: 23,428 x 7
## dateRep countriesAndTer… cases cumulative_cases deaths cumulative_deat…
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 01/01/… Afghanistan 0 0 0 0
## 2 01/02/… Afghanistan 0 0 0 0
## 3 01/03/… Afghanistan 0 0 0 0
## 4 01/04/… Afghanistan 0 0 0 0
## 5 01/05/… Afghanistan 0 0 0 0
## 6 01/06/… Afghanistan 0 0 0 0
## 7 01/07/… Afghanistan 0 0 0 0
## 8 01/08/… Afghanistan 0 0 0 0
## 9 01/09/… Afghanistan 0 0 0 0
## 10 01/10/… Afghanistan 0 0 0 0
## # … with 23,418 more rows, and 1 more variable: case_to_fatalities <dbl>view(cumulativeCovid5)cumulativeCovid <- tibble(
covid_entire %>%
group_by(countriesAndTerritories, "dateRep" = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d/%Y')) %>%
# since question is for each country and have to calculate cumualtive cases/deaths per day - grouping both variables
summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
# using cumsum() to calculate cumulative numbers each day for both 'cases' and 'deaths'
mutate("cumulative_cases" = cumsum(cases), "cumulative_deaths" = cumsum(deaths)) %>%
# using mutate() function to create two new variables
select(dateRep, countriesAndTerritories, cases, cumulative_cases, deaths, cumulative_deaths))
# selecting required variables to view and analyse## Graph of topTen countries: progress of cases till today:
today_total <- cumulativeCovid %>% group_by(countriesAndTerritories) %>% top_n(1, dateRep) %>% pull(cumulative_cases)
#cumulativeCovid %>% filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
# ggplot(mapping = aes(x = dateRep, y = cumulative_cases, color = countriesAndTerritories, group = countriesAndTerritories)) +
# geom_line(stat = "identity") + geom_text(stat = 'count', aes(label = today_total))
#scale_y_continuous(sec.axis = sec_axis(~ ., breaks = today_total))
cumulativeCovid %>%
filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
ggplot(mapping = aes(x = dateRep, y = cumulative_cases,
color = countriesAndTerritories, group = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
theme(plot.margin = unit(c(1,3,1,1), "lines")) +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
annotate(geom = 'text', x = 01/21/2020, y = 1, label = "..USA..", size = 4) +
labs(x = "Timeline", y = "Total Number of Cases", title = "Top Ten Countries: COVID-19",
subtitle = "Highest Number of Cases")
## How to label total today's cumulative total inside the graph?
## How to separate numbers that are getting cramped?
## How to label the date - when first case registered?# Case_to_fatalities graph
d_ends <- cumulativeCovid5 %>% group_by(countriesAndTerritories) %>% top_n(1, dateRep) %>% pull(case_to_fatalities)
#cumulativeCovid5 %>%
# filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
# ggplot(mapping = aes(x = dateRep, y = case_to_fatalities,
# group = countriesAndTerritories, color = countriesAndTerritories)) +
# geom_line(stat = "identity") + scale_y_continuous(sec.axis = sec_axis(~ ., breaks = d_ends)) +
# facet_wrap(~ countriesAndTerritories)
cumulativeCovid5 %>%
filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
ggplot(mapping = aes(x = dateRep, y = case_to_fatalities,
group = countriesAndTerritories, color = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
labs(x = "Timeline", y = "Death to Case Ratio / per 100", title = "Top 10 Countries Death to Case Ratio") +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3))## Warning: Removed 304 row(s) containing missing values (geom_path).
** ANALYSIS: USA being the front runner both in terms of number of cases and number of deaths. Another country from America continent (Brazil) has reached almost 0.9 million. European countries dominating the over all numbers (total 5 countries) in the top 10 list. Number of cases in India are growing rapidly in within short time span reached number 4 position.
========================== x ======================================
Question 6)
Use the mutate verb in dplyr to calculate the cumulative cases and deaths for each country. The new fields should be named cumulative_cases and cumulative_deaths respectively.
Answer 6)
#cumulativeCovid1 <- tibble(
# covid_entire %>%
# group_by(countriesAndTerritories, dateRep) %>%
# summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
# mutate("cumulative_cases" = cumsum(cases), "cumulative_deaths" = cumsum(deaths)) %>%
# select(dateRep, countriesAndTerritories, cases, cumulative_cases, deaths, cumulative_deaths)
#)
#cumulativeCovid1
#view(cumulativeCovid1)
## wrong way to have datewise cumualtive# Right format to have datewise cumualtive
cumulativeCovid <- tibble(
covid_entire %>%
group_by(countriesAndTerritories, "dateRep" = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d/%Y')) %>%
# since question is for each country and have to calculate cumualtive cases/deaths per day - grouping both variables
summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
# using cumsum() to calculate cumulative numbers each day for both 'cases' and 'deaths'
mutate("cumulative_cases" = cumsum(cases), "cumulative_deaths" = cumsum(deaths)) %>%
# using mutate() function to create two new variables
select(dateRep, countriesAndTerritories, cases, cumulative_cases, deaths, cumulative_deaths))
# selecting required variables to view and analyse
cumulativeCovid## # A tibble: 23,428 x 6
## dateRep countriesAndTerrito… cases cumulative_cases deaths cumulative_deat…
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 01/01/20… Afghanistan 0 0 0 0
## 2 01/02/20… Afghanistan 0 0 0 0
## 3 01/03/20… Afghanistan 0 0 0 0
## 4 01/04/20… Afghanistan 0 0 0 0
## 5 01/05/20… Afghanistan 0 0 0 0
## 6 01/06/20… Afghanistan 0 0 0 0
## 7 01/07/20… Afghanistan 0 0 0 0
## 8 01/08/20… Afghanistan 0 0 0 0
## 9 01/09/20… Afghanistan 0 0 0 0
## 10 01/10/20… Afghanistan 0 0 0 0
## # … with 23,418 more rows========================== x ======================================
Question 7)
Create a function called, casesByCountry(), that takes a user defined date and country code as its arguments and displays the distribution of cases for the selected country, leading up to the chosen date. Annotate the chart to show the highest number of cases that were reported; this should correspond with the daily reported case and not the cumulative cases. The chart should also contain a subtitle that indicates the population for the selected country. Note: if a date is not specified, make the current date the default. If a country is not specified, display a message to the user.
Answer 7)
1. user defined date()
2. user defined country code
3. distribution of cases in selected country
4. to date() ... can be today() ... going inlines with thought process in Q.3
5. Annotate the chart to show the highest number of cases that were reported; this should correspond with the daily reported case and not the cumulative cases: ?how to
6. The chart should also contain a subtitle that indicates the population for the selected country: labs() function
7. if a date is not specified, make the current date the default: else() argument
8. If a country is not specified, display a message to the user: else() argumentcaseByCountry <- function(ct, dt = format(today(), '%m/%d/%Y')) {
ct_len <- nchar(ct)
#print(x_len)
if(ct_len!='' & ct_len==3){
#print(class(x_len))
country_code <- covid_entire[covid_entire$countryterritoryCode == ct,]
} else {
return("Kindly enter Country Code")
}
date_parameter <- country_code[format(as.Date(country_code$dateRep, '%d/%m/%Y'), '%m/%d/%Y') <= dt,]
pop_18 <- date_parameter$popData2018
tot_cases <- sum(date_parameter$cases)
tot_deaths <- sum(date_parameter$deaths)
value <- max(date_parameter$cases)
if (value < 100){
y_value <- value + 5
}
else if(value<1000){
y_value <- value + 5
}
else if(value<50){
y_value <- value
}
else if(value >= 30000){
y_value <- value + 1000
}
else{
y_value <- value + 100
}
new <- date_parameter[date_parameter$cases == value,]
x_value <- format(as.Date(new$dateRep, '%d/%m/%Y'), '%m/%d')
#print(x_value)
#cumulative_deaths <- cumsum(date_parameter$deaths)
country_graph <- date_parameter %>%
ggplot(mapping = aes(x = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d'), y = cases)) +
geom_bar(stat = "identity", color = "black", fill = "grey", width=1, position=position_dodge()) +
geom_bar(data = subset(date_parameter,cases==max(cases)),stat = "identity",colour = "black", fill = "blue", width = 1, position = position_dodge()) +
#geom_line(aes(y = cumulative_deaths), stat = "identity", color = "Orange") +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
annotate(geom = 'text', x = x_value, dt, y=y_value, label=value, size = 3) +
labs(x = "Timeline", y = "Per-day Cases", title = paste(ct, ": COVID-19 Status as of", dt),
subtitle = paste("Population:", pop_18, "|", "Total Cases:", tot_cases, "|", "Total Deaths:", tot_deaths ))
#print(date_parameter)
print(country_graph)
}
caseByCountry("USA", "06/10/2020")## Warning: Ignoring unknown aesthetics: xmin
# Kindly enter 3-digit Country Code
# Kindly enter date in the format of '%m/%d/%Y'========================== x ======================================
Questions 8)
Select a country, of your choice, and use the casesByCountry() function to show the progression of the total COVID-19 cases to-date. Analyze the chart and the supporting data; indicate the total number of cases that were reported and the date of the first reported case. What is the current trend? • Based on your analysis for this country, what are the potential impact on countries like Andorra and San Marino. Create visualizations to support your analysis and/or add other supporting dataset(s) if necessary.
Answer 8)
- show the progression of the total COVID-19 cases to-date
- Analyze the chart and the supporting data
- indicate the total number of cases that were reported
- indicate the date of the first reported case
- the current trend?
- what are the potential impact on countries like Andorra and San Marino
- Create visualizations to support your analysis and/or add other supporting dataset(s) if necessary.
-> Examine the progression of the virus in a country of your preference. Then look at Andorra and San Marino and pay close attention to the information that is provided to you about these two countries/territories. Then explain the effect that the trends you observed in your preferred country, could have on Andorra and San Marino (should similar patterns emerge).
-> It may also be helpful to examine the continent that the two countries/territories are located, and if you examine the progression of the virus in a neighboring country, it may become clear why the potential impact is of importance.
# Country selected for Analysis: ITALY
newtenSub <- topTenCountries %>%
group_by(countryterritoryCode, dateRep, cases) %>%
filter(cases == 1 || cases==2 || cases==3)
unique(newtenSub)## # A tibble: 56 x 11
## # Groups: countryterritoryCode, dateRep, cases [56]
## dateRep day month year cases deaths countriesAndTer… countryterritor…
## <fct> <fct> <fct> <fct> <dbl> <dbl> <chr> <chr>
## 1 21/01/… 21 1 2020 1 0 United_States_o… USA
## 2 25/01/… 25 1 2020 1 0 United_States_o… USA
## 3 27/01/… 27 1 2020 3 0 United_States_o… USA
## 4 28/01/… 28 1 2020 1 0 Germany DEU
## 5 29/01/… 29 1 2020 3 0 Germany DEU
## 6 30/01/… 30 1 2020 1 0 India IND
## 7 31/01/… 31 1 2020 1 0 Germany DEU
## 8 31/01/… 31 1 2020 3 0 Italy ITA
## 9 31/01/… 31 1 2020 2 0 United_Kingdom GBR
## 10 31/01/… 31 1 2020 1 0 United_States_o… USA
## # … with 46 more rows, and 3 more variables: popData2018 <dbl>,
## # continentExp <fct>, case_to_fatalities <dbl>newtenSub[!duplicated(newtenSub$countryterritoryCode),]## # A tibble: 10 x 11
## # Groups: countryterritoryCode, dateRep, cases [10]
## dateRep day month year cases deaths countriesAndTer… countryterritor…
## <fct> <fct> <fct> <fct> <dbl> <dbl> <chr> <chr>
## 1 21/01/… 21 1 2020 1 0 United_States_o… USA
## 2 28/01/… 28 1 2020 1 0 Germany DEU
## 3 30/01/… 30 1 2020 1 0 India IND
## 4 31/01/… 31 1 2020 3 0 Italy ITA
## 5 31/01/… 31 1 2020 2 0 United_Kingdom GBR
## 6 01/02/… 1 2 2020 2 0 Russia RUS
## 7 01/02/… 1 2 2020 1 0 Spain ESP
## 8 20/02/… 20 2 2020 2 2 Iran IRN
## 9 26/02/… 26 2 2020 1 0 Brazil BRA
## 10 07/03/… 7 3 2020 1 0 Peru PER
## # … with 3 more variables: popData2018 <dbl>, continentExp <fct>,
## # case_to_fatalities <dbl># First day reported in Italy: January 31, 2020caseByCountry("ITA") ## Warning: Ignoring unknown aesthetics: xmin
** ANALYSIS: ITALY:
Total Population: 60,431,283
First Case Reported: January 31, 2020
Total Cases till date (06/14/2020): 236,305 (0.39% of population)
Total Deaths till date (06/14/2020): 34,223
Highest one-day new cases: 6,557
Progression of cases: Within 3 weeks of first reported case, the number of cases started rising. In the second week of March, the number of cases started increasing ‘exponentially’ displaying steep rise in the graph. Reaching it’s peak on March 22 with 6,557 cases in a single day. High number of cases (4,000 and above) remained for four weeks continuous and then showed ‘gradual’ decline. Italy considered as one of the top-most healthcare system couldn’t sustain this high influx of cases resulted into complete break-down of the healthcare system.
The disease took 7 weeks to reach to the peak, remained in peak phase for four weeks, and now almost since 9 weeks it is in declining phase. The country is still registering new cases daily ranging between 150-400.
# Italy: Progression of cases compared with topTen countries
cumulativeCovid %>%
filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
ggplot(mapping = aes(x = dateRep, y = cumulative_cases,
color = countriesAndTerritories, group = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
theme(plot.margin = unit(c(1,3,1,1), "lines")) +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
#annotate(geom = 'text', x = 01/21/2020, y = 1, label = "..USA..", size = 4) +
labs(x = "Timeline", y = "Total Number of Cases", title = "Top Ten Countries: COVID-19",
subtitle = "Highest Number of Cases")
# Italy with total 236,305 cases as of 06/14/2020 is 7th highest contributor in world and 4th highest contributor in Europe continent of COVID-19 cases. # Italy: Deaths compared with topTen countries
cumulativeCovid5 %>%
filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
ggplot(mapping = aes(x = dateRep, y = cumulative_deaths,
group = countriesAndTerritories, color = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
labs(x = "Timeline", y = "Total Deaths", title = "Top 10 Countries Deaths") +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3))
# Italy with total 34,223 as of 06/14/2020 is 4th highest contributor in world and 2nd highest contributor in Europe continent of deaths due to COVID-19. # Italy: Death to Case Ratio compared with topTen countries
cumulativeCovid5 %>%
filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
ggplot(mapping = aes(x = dateRep, y = case_to_fatalities,
group = countriesAndTerritories, color = countriesAndTerritories)) +
geom_line(stat = "identity") +
labs(x = "Timeline", y = "Death to Case Ratio / per 100", title = "Top 10 Countries Death to Case Ratio") +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3))## Warning: Removed 304 row(s) containing missing values (geom_path).
# Comparison of Death to Case Ratio with other countries in the world: The disease was more aggressive in Italy compared to other top ten contributor countries. United Kingdom is running almost similar ratio to that of Italy (14 deaths/per 100 cases). # Cumulative Cases: of European Countries
cumulativeEurope <- tibble(
covid_entire %>%
filter(continentExp == "Europe") %>%
group_by(countriesAndTerritories, "dateRep" = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d/%Y')) %>%
# since question is for each country and have to calculate cumualtive cases/deaths per day - grouping both variables
summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
# using cumsum() to calculate cumulative numbers each day for both 'cases' and 'deaths'
mutate("cumulative_cases" = cumsum(cases), "cumulative_deaths" = cumsum(deaths)) %>%
# using mutate() function to create two new variables
select(dateRep, countriesAndTerritories, cases, cumulative_cases, deaths, cumulative_deaths))
# selecting required variables to view and analyse
cumulativeEurope## # A tibble: 7,198 x 6
## dateRep countriesAndTerrito… cases cumulative_cases deaths cumulative_deat…
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 03/09/20… Albania 2 2 0 0
## 2 03/10/20… Albania 4 6 0 0
## 3 03/11/20… Albania 4 10 0 0
## 4 03/12/20… Albania 1 11 1 1
## 5 03/13/20… Albania 12 23 0 1
## 6 03/14/20… Albania 10 33 0 1
## 7 03/15/20… Albania 5 38 0 1
## 8 03/16/20… Albania 4 42 0 1
## 9 03/17/20… Albania 9 51 0 1
## 10 03/18/20… Albania 4 55 0 1
## # … with 7,188 more rows# Progression of Cases: Graph: Europe
cumulativeEurope %>%
ggplot(mapping = aes(x = dateRep, y = cumulative_cases,
color = countriesAndTerritories, group = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
theme(plot.margin = unit(c(1,3,1,1), "lines")) +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
labs(x = "Timeline", y = "Total Number of Cases", title = "European Countries: COVID-19",
subtitle = "Progression of Cases")
# Comparison of Italy with other European Countries:# Ten European Countries: Comparison graph: Progression of cases
cumulativeEurope %>%
filter(countriesAndTerritories %in% c("United_Kingdom", "Italy", "Spain", "France", "Germany", "Andorra", "San_Marino", "Switzerland", "Monaco", "Russia")) %>%
ggplot(mapping = aes(x = dateRep, y = cumulative_cases,
color = countriesAndTerritories, group = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
theme(plot.margin = unit(c(1,3,1,1), "lines")) +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
labs(x = "Timeline", y = "Total Number of Cases", title = "Ten European Countries: COVID-19",
subtitle = "Progression of Cases")
# Continent Europe: COVID-19 Status
covidEurope <- tibble(
covid_entire %>%
filter(continentExp == "Europe") %>%
group_by(countriesAndTerritories, "dateRep" = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d/%Y')) %>%
summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
mutate("cumulative_cases" = cumsum(cases),
"cumulative_deaths" = cumsum(deaths),
"case_to_fatalities" = (cumulative_deaths / cumulative_cases)*100) %>%
select(dateRep, countriesAndTerritories, cases, cumulative_cases,
deaths, cumulative_deaths, case_to_fatalities))
covidEurope## # A tibble: 7,198 x 7
## dateRep countriesAndTer… cases cumulative_cases deaths cumulative_deat…
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 03/09/… Albania 2 2 0 0
## 2 03/10/… Albania 4 6 0 0
## 3 03/11/… Albania 4 10 0 0
## 4 03/12/… Albania 1 11 1 1
## 5 03/13/… Albania 12 23 0 1
## 6 03/14/… Albania 10 33 0 1
## 7 03/15/… Albania 5 38 0 1
## 8 03/16/… Albania 4 42 0 1
## 9 03/17/… Albania 9 51 0 1
## 10 03/18/… Albania 4 55 0 1
## # … with 7,188 more rows, and 1 more variable: case_to_fatalities <dbl># Progression of Cases: Graph: Europe
cumulativeEurope %>%
ggplot(mapping = aes(x = dateRep, y = cumulative_cases,
color = countriesAndTerritories, group = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
theme(plot.margin = unit(c(1,3,1,1), "lines")) +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
labs(x = "Timeline", y = "Total Number of Cases", title = "European Countries: COVID-19",
subtitle = "Progression of Cases")
# Comparison of Italy with other European Countries:# Progression of Cases: Graph: Ten European Countries
covidEurope %>%
filter(countriesAndTerritories %in% c("United_Kingdom", "Italy", "Spain", "France", "Germany", "Andorra", "San_Marino", "Switzerland", "Monaco", "Russia")) %>%
ggplot(mapping = aes(x = dateRep, y = cumulative_cases,
color = countriesAndTerritories, group = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
theme(plot.margin = unit(c(1,3,1,1), "lines")) +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
labs(x = "Timeline", y = "Total Number of Cases", title = "Ten European Countries: COVID-19",
subtitle = "Progression of Cases")
# Deaths: Graph: Ten European Countries
covidEurope %>%
filter(countriesAndTerritories %in% c("United_Kingdom", "Italy", "Spain", "France", "Germany", "Andorra", "San_Marino", "Switzerland", "Monaco", "Russia")) %>%
ggplot(mapping = aes(x = dateRep, y = cumulative_deaths,
color = countriesAndTerritories, group = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
theme(plot.margin = unit(c(1,3,1,1), "lines")) +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
labs(x = "Timeline", y = "Total Number of Deaths", title = "Ten European Countries: Deaths: COVID-19")
# Italy: second hihgest contributor in Europe after United Kingdom. # # Death to Case Ratio: Graph: Ten European Countries
covidEurope %>%
filter(countriesAndTerritories %in% c("United_Kingdom", "Italy", "Spain", "France", "Germany", "Andorra", "San_Marino", "Switzerland", "Monaco", "Russia")) %>%
ggplot(mapping = aes(x = dateRep, y = case_to_fatalities,
group = countriesAndTerritories, color = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
labs(x = "Timeline", y = "Death to Case Ratio / per 100", title = "Ten European Countries: COVID-19", subtitle = "Death to Case Ratio") +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3))## Warning: Removed 346 row(s) containing missing values (geom_path).
# Death to Case Ratio: of Italy remained second highest in the Europe continent with 14 deaths/per 100 cases registered. This high ratio also indicates vulnerability of the population which is aged. # Mortality Rate: Ten European Countries:
europeMortality <- tibble(
covid_entire %>% filter(countriesAndTerritories %in% c("United_Kingdom", "Italy", "Spain", "France", "Germany", "Andorra", "San_Marino", "Switzerland", "Monaco", "Russia")) %>%
group_by(dateRep, countriesAndTerritories) %>%
summarise(cases = sum(cases), deaths = sum(deaths),
population = sum(popData2018)) %>%
mutate("mortalityRate" = ((deaths/population) * 100000)) %>% # 'denominator' for Mortality rate specific to any disease
select(dateRep, countriesAndTerritories, cases, deaths, population, mortalityRate))
europeMortality## # A tibble: 1,582 x 6
## dateRep countriesAndTerritories cases deaths population mortalityRate
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 01/01/2020 France 0 0 66987244 0
## 2 01/01/2020 Germany 0 0 82927922 0
## 3 01/01/2020 Italy 0 0 60431283 0
## 4 01/01/2020 Monaco 0 0 38682 0
## 5 01/01/2020 Russia 0 0 144478050 0
## 6 01/01/2020 San_Marino 0 0 33785 0
## 7 01/01/2020 Spain 0 0 46723749 0
## 8 01/01/2020 Switzerland 0 0 8516543 0
## 9 01/01/2020 United_Kingdom 0 0 66488991 0
## 10 01/02/2020 France 0 0 66987244 0
## # … with 1,572 more rows# Mortality Rate: Graph: Ten European Countries:
europeMortality %>%
ggplot(mapping = aes(y = mortalityRate, x = cases, color = countriesAndTerritories)) +
geom_point() + geom_smooth(aes(color = mortalityRate)) + facet_wrap(~ countriesAndTerritories) +
#ylim(0, 10) +
labs(x = "No. of Cases", y = "Mortality Rate / 100,000 Population",
title = "Progression of Cases & Mortality Rate: Europe")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at -0.045## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 0.002025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at -0.045## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.045## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)
===================== x ===========================
ANDORRA:
Andorra is the sixth-smallest nation in Europe, having an area of 468 square kilometres (181 sq mi) and a population of approximately 77,006. The Andorran people are a Romance ethnic group of originally Catalan descent. Andorra is the 16th-smallest country in the world by land and the 11th-smallest by population. Andorra is a tiny country in south western Europe, located in the eastern Pyrenees mountains and bordered by “Spain” and “France”.
** Andorra had the highest life expectancy in the world at 81 years, according to the Global Burden of Disease Study
caseByCountry("AND")## Warning: Ignoring unknown aesthetics: xmin
** ANALYSIS: ANDORRA:
Total Population: 77,006
First Case Reported: March 3, 2020
Total Cases till date (06/14/2020): 853 (1.1% of population)
Total Deaths till date (06/14/2020): 51
Highest one-day new cases: 79
The Trend: The first case reported on March 3 and reached its peak in 3rd week. the peak phase lasted for about 3 weeks and then cases started declining gradually. May month remianed silent comparatively and there’s a sudden spike of 79 cases (highest one day total) on June 3, 2020. This might be the reason that these cases were diagnosed in May month, but reporting done on June 3?
With 853 cases, we can say that around 1.1% of the population got infected with the disease. This is usually an issue with the country having small population. The imapct is larger comapred to countries with high popualtion. If we consider Mortality rate (which is counted with 100,000 as denominator), even with one death, it will create bigger impact on the ratio. Again, this is evident looking at Mortality rate graph that indicates Andorra mortality of upto 5.5 against neigbouring countries France and Spain (Mortality rate of 2)
With 51 deaths out of 853 cases, Death to Case Ratio of Andorra is around 6, while France (18) and Spain (12) showed much higher death to case ratio.
# Neighbouring Countries: Spain and France
caseByCountry("ESP")## Warning: Ignoring unknown aesthetics: xmin
caseByCountry("FRA")## Warning: Ignoring unknown aesthetics: xmin
# Andorra: Mortality Rate in comaprison with neighbouring countries:
covid_entire %>% filter(countriesAndTerritories %in% c("Spain", "France", "Andorra")) %>%
group_by(dateRep, countriesAndTerritories) %>%
summarise(cases = sum(cases), deaths = sum(deaths),
population = sum(popData2018)) %>%
mutate("mortalityRate" = ((deaths/population) * 100000)) %>%
ggplot(mapping = aes(y = mortalityRate, x = cases, color = countriesAndTerritories)) +
geom_point(size = 3) +
#ylim(0, 10) +
labs(x = "No. of Cases", y = "Mortality Rate / 100,000 Population",
title = "Progression of Cases & Mortality Rate: Andorra")
# Andorra: Death to Case ratio: in comaprison with neighbouring countries:
covidEurope %>%
filter(countriesAndTerritories %in% c("Spain", "France", "Andorra")) %>%
ggplot(mapping = aes(x = dateRep, y = case_to_fatalities,
group = countriesAndTerritories, color = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
labs(x = "Timeline", y = "Death to Case Ratio / per 100", title = "European Country: Andorra", subtitle = "Death to Case Ratio") +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3))## Warning: Removed 55 row(s) containing missing values (geom_path).
===================== x ===========================
SAN MARINO:
San Marino, officially the Republic of San Marino, also known as the Most Serene Republic of San Marino, is a country in Southern Europe completely enclosed by “Italy”. It is one of only three countries in the world to be completely enclosed by another country. It is the third smallest country in Europe, after Vatican City and Monaco, and the fifth smallest country in the world. San Marino covers a land area of just over 61 km2 (24 sq mi), and has a population of 33,562
caseByCountry("SMR")## Warning: Ignoring unknown aesthetics: xmin
** ANALYSIS: SAN MARIO: Compare with neighbouring country: Italy
Total Population: 33,785
First Case Reported: February 28, 2020
Total Cases till date (06/14/2020): 703 (2.1% of population)
Total Deaths till date (06/14/2020): 42
Highest one-day new cases: 36
The Trend: The first case reported on February 28 i.e. 4 weeks after the frist case reported in Italy. and reached its peak in 3rd week of March. It was at the same time, where cases in Italy were at peak and registered its highest one-day cases on March 22. The country continues showing high pillars (high number of cases) intermittently unlike the neighbouring country (Italy) that is showing a definite declining trend.
With 703 cases, we can say that around 2.1% of the population got infected with the disease. This is usually an issue with the country having small population that we mentioned in analysis of Andorra. The imapct is larger comapred to countries with high popualtion. Again, this is evident looking at Mortality rate graph that indicates Andorra mortality of upto 17.5 against neigbouring country Italy (Mortality rate of less than 2)
With 36 deaths out of 703 cases, Death to Case Ratio of San Marino is around 6, while Italy (14) showed much higher death to case ratio.
# San Marino: Mortality Rate in comaprison with neighbouring countries:
covid_entire %>% filter(countriesAndTerritories %in% c("Italy", "San_Marino")) %>%
group_by(dateRep, countriesAndTerritories) %>%
summarise(cases = sum(cases), deaths = sum(deaths),
population = sum(popData2018)) %>%
mutate("mortalityRate" = ((deaths/population) * 100000)) %>%
ggplot(mapping = aes(y = mortalityRate, x = cases, color = countriesAndTerritories)) +
geom_point(size = 3) +
#ylim(0, 10) +
labs(x = "No. of Cases", y = "Mortality Rate / 100,000 Population",
title = "Progression of Cases & Mortality Rate: San Marino")
# San Marino: Death to Case ratio: in comaprison with neighbouring countries:
covidEurope %>%
filter(countriesAndTerritories %in% c("Italy", "San_Marino")) %>%
ggplot(mapping = aes(x = dateRep, y = case_to_fatalities,
group = countriesAndTerritories, color = countriesAndTerritories)) +
geom_line(stat = "identity", size = 1.2) +
labs(x = "Timeline", y = "Death to Case Ratio / per 100", title = "European Country: San Marino", subtitle = "Death to Case Ratio") +
theme(axis.text.x = element_text(angle=65, vjust=1,size=3))## Warning: Removed 88 row(s) containing missing values (geom_path).